Dynamic Neural-Model-Based Predictive Control for Autonomous Wheel-Legged Robot System

Mobile wheel-legged robots exhibiting mobility, stability and reliability have garnered heightened research attention in demanding real-world scenarios, especially in material transport, emergency response and space exploration. The kinematics model merely delineates the geometric relationship of the controlled objective, disregarding force feedback. This study investigates model predictive trajectory tracking control utilising the robot dynamic model (DRMPC) in the context of unpredictable interactions. The predictive tracking controller for the wheel-legged robot is introduced in the context of position tracking. A dynamic approximator is employed to address the uncertain interactions in the tracking process. Ultimately, co-simulation and empirical tests are conducted to demonstrate the efficacy of the devised control methodology, which achieves high precision and dependable robustness. This work can elucidate the technical and practical oversight of autonomous movement in complicated environments and enhance the manoeuverability and flexibility.